Self-Driving Car Engineer Nanodegree¶
Deep Learning¶
Project: Build a Traffic Sign Recognition Classifier¶
In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
Step 0: Load The Data¶
# Load pickled data
import pickle
training_file = "traffic-signs-data/train.p"
validation_file = "traffic-signs-data/valid.p"
testing_file = "traffic-signs-data/test.p"
sign_names_file = 'signnames.csv'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
with open(sign_names_file) as f:
class_names = list(map(lambda x: x.strip().split(',')[1], f.readlines()))[1:]
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
Step 1: Dataset Summary & Exploration¶
The pickled data is a dictionary with 4 key/value pairs:
'features'is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).'labels'is a 1D array containing the label/class id of the traffic sign. The filesignnames.csvcontains id -> name mappings for each id.'sizes'is a list containing tuples, (width, height) representing the original width and height the image.'coords'is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES
Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.
import numpy as np
n_train = len(X_train)
n_valid = len(X_valid)
n_test = len(X_test)
image_shape = X_train[0].shape
n_classes = len(np.unique(y_train))
print("Number of training examples =", n_train)
print("Number of validation examples =", n_valid)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas¶
Include an exploratory visualization of the dataset¶
Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.
The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.
NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?
Step 2: Design and Test a Model Architecture¶
Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.
The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!
With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.
There are various aspects to consider when thinking about this problem:
- Neural network architecture (is the network over or underfitting?)
- Play around preprocessing techniques (normalization, rgb to grayscale, etc)
- Number of examples per label (some have more than others).
- Generate fake data.
Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.
import matplotlib.pyplot as plt
%matplotlib inline
indexes = np.argwhere(y_train == 0)
plt.figure(figsize=(10, 30))
plt.imshow(np.hstack(X_train[indexes[0:15, 0]]))
plt.axis('off')
plt.show()
from sklearn.utils import shuffle
# the sample image from each class
fig, ax = plt.subplots(figsize=(15, 30))
examples = []
for cls in range(n_classes):
indexes = np.argwhere(y_train == cls)
np.random.shuffle(indexes)
examples.append(np.hstack(X_train[indexes[0:15, 0]]))
ax.imshow(np.vstack(examples))
ax.set_yticks(np.arange(n_classes) * image_shape[0] + image_shape[0] / 2)
ax.set_yticklabels(class_names)
ax.set_xticks([])
plt.show()
# histogram
plt.hist(y_train, bins=n_classes, rwidth=0.8)
plt.show()
print("min train size is {}.".format(np.min([np.sum(y_train == i) for i in range(n_classes)])))
plt.hist(y_valid, bins=n_classes, rwidth=0.8)
plt.show()
Pre-process the Data Set (normalization, grayscale, etc.)¶
Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128 is a quick way to approximately normalize the data and can be used in this project.
Other pre-processing steps are optional. You can try different techniques to see if it improves performance.
Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.
print('# Original data')
print('Shape:', X_train.shape)
print('Average:', np.mean(X_train))
import cv2
def grayscale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def equalize_hist(img):
return cv2.equalizeHist(img)
def normalize(img):
min_, max_ = np.min(img), np.max(img)
return (img - min_) / (max_ - min_) * 2 - 1
def preprocess_each(img):
return np.expand_dims(normalize(equalize_hist(grayscale(img))), axis=2)
def preprocess(dataset):
return np.array([preprocess_each(img) for img in dataset])
def one_hot_encoder(y):
one_hot_y = np.zeros((len(y), n_classes))
one_hot_y[range(len(y)), y] = 1
return one_hot_y
X_train = preprocess(X_train)
X_valid = preprocess(X_valid)
X_test = preprocess(X_test)
print('# Preprocessed data')
print('Shape:', X_train.shape)
print('Average:', np.mean(X_train))
y_one_hot_train = one_hot_encoder(y_train)
y_one_hot_valid = one_hot_encoder(y_valid)
y_one_hot_test = one_hot_encoder(y_test)
fig, ax = plt.subplots(figsize=(10, 30))
examples = []
for cls in range(n_classes):
indexes = np.argwhere(y_train == cls)
np.random.shuffle(indexes)
examples.append(np.hstack(X_train[indexes[0:15, 0]][:, :, :, 0]))
ax.imshow(np.vstack(examples), cmap='gray')
ax.set_yticks(np.arange(n_classes) * image_shape[0] + image_shape[0] / 2)
ax.set_yticklabels(class_names)
ax.set_xticks([])
plt.show()
Data Augumentation¶
from tqdm import tqdm
alpha = 1.
augmented_X = []
augmented_y = []
for _ in tqdm(range(5000)):
indexes = np.arange(n_train)
np.random.shuffle(indexes)
i, j = indexes[:2]
lambda_ = np.random.beta(alpha, alpha)
augmented_X.append(normalize(X_train[i] * lambda_ + X_train[j] * (1 - lambda_)))
augmented_y.append(y_one_hot_train[i] * lambda_ + y_one_hot_train[j] * (1 - lambda_))
plt.imshow(augmented_X[200][:,:,0], cmap='gray')
print("Mixing [{}] and [{}]".format(class_names[1], class_names[28]))
X_train = np.concatenate([X_train, np.array(augmented_X)])
y_one_hot_train = np.concatenate([y_one_hot_train, np.array(augmented_y)])
Model Architecture¶
import tensorflow as tf
from tensorflow.contrib.layers import flatten
def batch_normalization(inputs, is_training=None, decay=0.99):
epsilon = 1e-5
out_dim = inputs.get_shape()[-1]
scale = tf.Variable(tf.ones([out_dim]))
beta = tf.Variable(tf.zeros([out_dim]))
pop_mean = tf.Variable(tf.zeros([out_dim]), trainable=False)
pop_var = tf.Variable(tf.ones([out_dim]), trainable=False)
if is_training == None:
return tf.nn.batch_normalization(inputs, pop_mean, pop_var, beta, scale, epsilon)
axes = list(range(len(inputs.get_shape()) - 1))
batch_mean, batch_var = tf.nn.moments(inputs, axes)
ema = tf.train.ExponentialMovingAverage(decay=decay)
def update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.nn.batch_normalization(inputs, tf.identity(batch_mean), tf.identity(batch_var), beta, scale, epsilon)
def average():
train_mean = pop_mean.assign(ema.average(batch_mean))
train_var = pop_var.assign(ema.average(batch_var))
with tf.control_dependencies([train_mean, train_var]):
return tf.nn.batch_normalization(inputs, train_mean, train_var, beta, scale, epsilon)
return tf.cond(is_training, update, average)
def weight_variable(shape):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
return tf.Variable(tf.truncated_normal(shape=shape, mean = 0, stddev = 0.1))
def conv2d(x, W, k):
return tf.nn.conv2d(x, W, strides=[1, k, k, 1], padding='VALID')
def max_pool(x, k):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')
def LeNet(x):
# SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
conv1_W = weight_variable(shape=(5, 5, 1, 6))
conv1 = conv2d(x, conv1_W, k=1)
conv1 = batch_normalization(conv1, is_training)
# SOLUTION: Activation.
conv1 = tf.nn.relu(conv1)
# SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = max_pool(conv1, k=2)
# SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
conv2_W = weight_variable(shape=(5, 5, 6, 16))
conv2 = conv2d(conv1, conv2_W, k=1)
conv2 = batch_normalization(conv2, is_training)
# SOLUTION: Activation.
conv2 = tf.nn.relu(conv2)
# SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = max_pool(conv2, k=2)
# SOLUTION: Flatten. Input = 5x5x16. Output = 400.
fc0 = flatten(conv2)
# SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
fc1_W = weight_variable(shape=(400, 120))
fc1 = tf.matmul(fc0, fc1_W)
fc1 = batch_normalization(fc1, is_training)
# SOLUTION: Activation.
fc1 = tf.nn.relu(fc1)
# SOLUTION: Dropout.
fc1 = tf.nn.dropout(fc1, keep_prob)
# SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
fc2_W = weight_variable(shape=(120, 84))
fc2 = tf.matmul(fc1, fc2_W)
fc2 = batch_normalization(fc2, is_training)
# SOLUTION: Activation.
fc2 = tf.nn.relu(fc2)
# SOLUTION: Dropout.
fc2 = tf.nn.dropout(fc2, keep_prob)
# SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43.
fc3_W = weight_variable(shape=(84, 43))
fc3_b = tf.Variable(tf.zeros(43))
logits = tf.matmul(fc2, fc3_W) + fc3_b
return logits
Train, Validate and Test the Model¶
A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.
x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.float32, (None, n_classes))
is_training = tf.placeholder(tf.bool)
keep_prob = tf.placeholder(tf.float32)
rate = 0.03
EPOCHS = 50
BATCH_SIZE = 128
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation,
feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0, is_training: False})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
history_train = []
history_valid = []
history_test = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_one_hot_train = shuffle(X_train, y_one_hot_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_one_hot_train[offset:end]
sess.run(training_operation,
feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5, is_training: True})
training_accuracy = evaluate(X_train, y_one_hot_train)
validation_accuracy = evaluate(X_valid, y_one_hot_valid)
test_accuracy = evaluate(X_test, y_one_hot_test)
history_train.append(training_accuracy)
history_valid.append(validation_accuracy)
history_test.append(test_accuracy)
if (i+1) % 5 == 0:
print("EPOCH {} ...".format(i+1))
print("Training Accuracy = {:.3f}".format(training_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print("Test Accuracy = {:.3f}".format(test_accuracy))
print()
saver.save(sess, './lenet')
print("Model saved")
plt.figure(figsize=(10,6))
plt.plot(range(1, EPOCHS+1), history_train)
plt.plot(range(1, EPOCHS+1), history_valid)
plt.plot(range(1, EPOCHS+1), history_test)
plt.legend(['training', 'validation', 'test'], loc='lower right')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.xlim([1, EPOCHS])
plt.show()
Step 3: Test a Model on New Images¶
To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.
You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.
Load and Output the Images¶
from pathlib import Path
test_images = []
test_labels = np.array([23, 12, 3, 35, 25])
files = sorted(Path('test_images/').glob('*'))
fig, ax = plt.subplots(1, 5, figsize=(15, 8))
ax = ax.ravel()
for i, file in enumerate(files):
img = cv2.cvtColor(cv2.imread(file.as_posix()), cv2.COLOR_BGR2RGB)
ax[i].imshow(img)
ax[i].set_title(class_names[test_labels[i]])
ax[i].axis('off')
test_images.append(img)
test_images = preprocess(test_images)
Predict the Sign Type for Each Image¶
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
predictions = sess.run(tf.argmax(logits, 1),
feed_dict={x: test_images, keep_prob: 1.0, is_training: False})
print("")
for (test_label, prediction) in zip(test_labels, predictions):
if test_label == prediction:
print("[PASS]")
else:
print("[FAIL] test_label: {} ({}), prediction: {} ({})".format(test_label,
class_names[test_label],
prediction,
class_names[prediction]))
Analyze Performance¶
Test Accuracy (new images) = 0.800
Output Top 5 Softmax Probabilities For Each Image Found on the Web¶
For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k could prove helpful here.
The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.
tf.nn.top_k will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.
Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tf.nn.top_k is used to choose the three classes with the highest probability:
# (5, 6) array
a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
0.12789202],
[ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
0.15899337],
[ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
0.23892179],
[ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
0.16505091],
[ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
0.09155967]])Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3)) produces:
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
[ 0.28086119, 0.27569815, 0.18063401],
[ 0.26076848, 0.23892179, 0.23664738],
[ 0.29198961, 0.26234032, 0.16505091],
[ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
[0, 1, 4],
[0, 5, 1],
[1, 3, 5],
[1, 4, 3]], dtype=int32))Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202], you can confirm these are the 3 largest probabilities in a. You'll also notice [3, 0, 5] are the corresponding indices.
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
top_k_outputs = sess.run(tf.nn.top_k(tf.nn.softmax(logits), k=5),
feed_dict={x: test_images, keep_prob: 1.0, is_training: False})
print(top_k_outputs.values)
print()
print(top_k_outputs.indices)
Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.